Assessing the potential of heliospheric imager data assimilation to improve CME modelling.

Modelling the heliospheric evolution of Coronal Mass Ejections (CMEs) is challenging and fraught with uncertainty for a range of confounding reasons. For example, there are significant uncertainties in the boundary conditions of heliospheric numerical models and such models often use CME parameterisations which are known to be overly simplistic e.g. hydrodynamic perturbations without magnetic structure. Consequently, the uncertainty on modelled CME evolution remains high and simulations often struggle to accurately represent both in-situ and remotely sensed CME observations.

Data assimilation (DA) provides a framework for merging observations and a model of a system to return simulations that better represent the true state of a system. We are exploring how to use data assimilation techniques with the white-light Heliospheric Imager (HI) CME observations to generate CME simulations that better represent the observed evolution of CMEs.

Here we present the results of a set of Observing System Simulation Experiments that begin to quantify the potential gains from assimilating HI data into the HUXt solar wind model, using a particle filter data assimilation scheme based on Sequential Importance Resampling.

We explore several specific questions: 1) By how much does the HI DA improve the simulated kinematics profiles of CMEs. 2) From which observing location are HI data best able to improve simulations of Earth directed CMEs. 3) Is it necessary to assimilate the full HI image data, or is it sufficient to assimilate HI derived data products, such as the time-elongation profile of the CME front.